Whole-genome sequencing coupled to imputation discovers genetic signals for anthropometric traits

Publication type

Journal Article

Authors

Publication date

June 1, 2017

Summary:

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of twelve anthropometric traits associated with height, body mass and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. Seventy-one percent of signals reside within genes and fine-mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits, and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically-relevant discoveries across the frequency spectrum.

Published in

American Journal of Human Genetics

Volume and page numbers

Volume: 100 , p.865 -884

DOI

http://dx.doi.org/10.1016/j.ajhg.2017.04.014

ISSN

29297

Subjects

Notes

Open Access

Open Access funded by Wellcome Trust

Under a Creative Commons license

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